Evidence map›Paper›PMID 41346341›Full record

ArticleNPJ clean water2025

Microplastic removal across ten drinking water treatment facilities and distribution systems.

Charles Balkenbusch, Judith Glienke, Yuhao Wu, Keenan Munno, Michael Jung, Husein Almuhtaram, Robert C Andrews

Abstract read
In one paragraph

Article in NPJ clean water, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Charles Balkenbusch *Department of Civil and Mineral Engineering, University of Toronto, Toronto, ON Canada.
Judith Glienke *Department of Civil and Mineral Engineering, University of Toronto, Toronto, ON Canada.
Yuhao WuDepartment of Civil and Mineral Engineering, University of Toronto, Toronto, ON Canada.
Keenan MunnoDepartment of Civil and Mineral Engineering, University of Toronto, Toronto, ON Canada.
Michael JungDepartment of Civil and Mineral Engineering, University of Toronto, Toronto, ON Canada.
Husein AlmuhtaramDepartment of Civil and Mineral Engineering, University of Toronto, Toronto, ON Canada.
Robert C AndrewsDepartment of Civil and Mineral Engineering, University of Toronto, Toronto, ON Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The performance of conventional and advanced drinking water treatment processes for the removal of microplastics is poorly understood due to the use of a wide range of methods for sample collection, isolation, and analysis that make direct comparison among studies challenging. In this study, microplastic (>2 µm) removal across ten drinking water treatment facilities, as well as their presence in source waters and distribution systems, was characterized. Municipal drinking water treatment facilities achieved >97.5% removal, primarily due to chemically assisted granular media filtration or ultrafiltration. In untreated source waters, concentrations ranged from 1193 ± 64 to 7185 ± 64 particles/L, with polypropylene, polyethylene, polyamide, and plastic copolymers representing the most common polymer types identified. These findings provide insight regarding microplastic exposure via drinking water, as well as treatment process performance for their removal which may be used to inform the development and implementation of future regulations and/or guidelines.

Indexed as

ChemistryEngineeringEnvironmental sciencesMaterials scienceWater resources

Identifiers

PMID41346341
PMCPMC12673860

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.